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GSgalgoR

An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer


Bioconductor version: Release (3.18)

A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The 'Galgo' framework combines the advantages of clustering algorithms for grouping heterogeneous 'omics' data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high.

Author: Martin Guerrero [aut], Carlos Catania [cre]

Maintainer: Carlos Catania <harpomaxx at gmail.com>

Citation (from within R, enter citation("GSgalgoR")):

Installation

To install this package, start R (version "4.3") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GSgalgoR")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("GSgalgoR")
GSgalgoR.html HTML R Script
GSgalgoR_callbacks.html HTML R Script
Reference Manual PDF
NEWS Text
LICENSE Text

Details

biocViews Classification, Clustering, GeneExpression, Software, Survival, Transcription
Version 1.12.0
In Bioconductor since BioC 3.12 (R-4.0) (3.5 years)
License MIT + file LICENSE
Depends
Imports cluster, doParallel, foreach, matchingR, nsga2R, survival, proxy, stats, methods
System Requirements
URL https://github.com/harpomaxx/GSgalgoR
Bug Reports https://github.com/harpomaxx/GSgalgoR/issues
See More
Suggests knitr, rmarkdown, ggplot2, BiocStyle, genefu, survcomp, Biobase, survminer, breastCancerTRANSBIG, breastCancerUPP, iC10TrainingData, pamr, testthat
Linking To
Enhances
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package GSgalgoR_1.12.0.tar.gz
Windows Binary GSgalgoR_1.12.0.zip
macOS Binary (x86_64) GSgalgoR_1.12.0.tgz
macOS Binary (arm64) GSgalgoR_1.12.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/GSgalgoR
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/GSgalgoR
Bioc Package Browser https://code.bioconductor.org/browse/GSgalgoR/
Package Short Url https://bioconductor.org/packages/GSgalgoR/
Package Downloads Report Download Stats